NDLI: Error handling approach using characterization and correction steps for handwritten document analysis

Content Provider	Springer Nature Link
Author	Quiniou, Solen Cheriet, Mohamed Anquetil, Eric
Copyright Year	2011
Abstract	In this paper, we present a framework to handle recognition errors from a N-best list of output phrases given by a handwriting recognition system, with the aim to use the resulting phrases as inputs to a higher-level application. The framework can be decomposed into four main steps: phrase alignment, detection, characterization, and correction of word error hypotheses. First, the N-best phrases are aligned to the top-list phrase, and word posterior probabilities are computed and used as confidence indices to detect word error hypotheses on this top-list phrase (in comparison with a learned threshold). Then, the errors are characterized into predefined types, using the word posterior probabilities of the top-list phrase and other features to feed a trained SVM. Finally, the final output phrase is retrieved, thanks to a correction step that used the characterized error hypotheses and a designed word-to-class backoff language model. First experiments were conducted on the ImadocSen-OnDB handwritten sentence database and on the IAM-OnDB handwritten text database, using two recognizers. We present first results on an implementation of the proposed framework for handling recognition errors on transcripts of handwritten phrases provided by recognition systems.
Starting Page	125
Ending Page	141
Page Count	17
File Format	PDF
ISSN	14332833
Journal	International Journal of Document Analysis and Recognition (IJDAR)
Volume Number	15
Issue Number	2
e-ISSN	14332825
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-04-07
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Handwriting recognition Error processing Confidence score Word posterior probability Error characterization Language model Image Processing and Computer Vision Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications Computer Vision and Pattern Recognition Software

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Error handling approach using characterization and correction steps for handwritten document analysis

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